NiaAutoARM: Automated generation and evaluation of Association Rule Mining pipelines
This work addresses the problem of automating complex pipeline steps in association rule mining for data analysts, but it appears incremental as it applies existing AutoML concepts to a specific domain.
The authors tackled the challenge of automating the construction of association rule mining pipelines for datasets with both numerical and categorical attributes, proposing NiaAutoARM, a method based on stochastic population-based meta-heuristics, and conducted a comprehensive experimental evaluation.
The Numerical Association Rule Mining paradigm that includes concurrent dealing with numerical and categorical attributes is beneficial for discovering associations from datasets consisting of both features. The process is not considered as easy since it incorporates several processing steps running sequentially that form an entire pipeline, e.g., preprocessing, algorithm selection, hyper-parameter optimization, and the definition of metrics evaluating the quality of the association rule. In this paper, we proposed a novel Automated Machine Learning method, NiaAutoARM, for constructing the full association rule mining pipelines based on stochastic population-based meta-heuristics automatically. Along with the theoretical representation of the proposed method, we also present a comprehensive experimental evaluation of the proposed method.